Comparison between primary care service delivery in Malaysia and other participating countries of the QUALICOPC project: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Most countries including Malaysia have set goals to incorporate a strong primary care into the healthcare system. The aim of this study was to evaluate the strength of service delivery process dimensions in Malaysia and compare it with England, the Netherlands, Spain, North Macedonia, Romania and Turkey which participated in the Quality and Costs of Primary Care (QUALICOPC) study. METHODS: This cross-sectional study utilised the QUALICOPC study data on primary care performance, which was conducted in 2011-2013 (QUALICOPC in Europe Australia, New Zealand and Canada) and 2015-2016 (Malaysia). A standardised questionnaire was completed by primary care practitioners from participating countries. Multilevel regression analysis and composite scores were constructed to compare the performance of primary care on four process dimensions: accessibility, comprehensiveness, continuity of care and coordination. RESULTS: The high-income countries with strong primary care performed better in comprehensiveness, continuity and coordination but poorer in accessibility to services compared with upper-middle-income countries. Among the upper-middle-income countries, Malaysia scored the best in comprehensiveness and coordination. None of the studied countries were having consistent performance over all indicators either in their respective best or worst primary care services delivery dimensions. CONCLUSIONS: There is a wide variation in primary care services delivery across and within the studied countries. The findings indicate room for quality improvement activities to strengthen primary healthcare services. This includes addressing current healthcare challenges in response to the population health needs which are essential for more integrated and efficient primary care services delivery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".